Datasets:

MicPie
/

unpredictable_sporcle-com

	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""This loads the AdapTable-sporcle.com dataset."""

	import json
	import os
	import pandas as pd

	import datasets


	# TODO: Add BibTeX citation
	_CITATION = """\
	@InProceedings{huggingface:dataset,
	title = {TODO: UPDATE TITLE HERE},
	author={TODO: BUT AUTHORS HERE},
	year={2020}
	}
	"""

	# TODO: Update description
	_DESCRIPTION = """\
	The AdapTable dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.
	"""

	# TODO: Add a link to an official homepage for the dataset here
	_HOMEPAGE = ""

	_LICENSE = "Apache 2.0"

	_URL = "https://huggingface.co/datasets/MicPie/adaptable_sporcle.com/resolve/main/data/adaptable_sporcle.com.jsonl"

	logger = datasets.logging.get_logger(__name__)


	class AdapTableSite(datasets.GeneratorBasedBuilder):
	# TODO: Update docs:
	"""
	The AdapTable dataset consists of tables that naturally occur on the web, that are formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.
	"""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	features = datasets.Features(
	{
	"task": datasets.Value("string"),
	"input": datasets.Value("string"),
	"output": datasets.Value("string"),
	"options": datasets.Sequence([datasets.Value("string")]),
	"pageTitle": datasets.Value("string"),
	"outputColName": datasets.Value("string"),
	"url": datasets.Value("string"),
	"wdcFile": datasets.Value("string")
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	data_dir = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"filepath": data_dir},
	),
	]

	def _generate_examples(self, filepath):
	"""Yields examples."""
	with open(filepath, encoding="utf-8") as f:
	for i, row in enumerate(f):
	data = json.loads(row)
	key = f"{data['task']}_{i}"
	yield key, {
	"task": data["task"],
	"input": data["input"],
	"output": data["output"],
	"options": data["options"],
	"pageTitle": data["pageTitle"],
	"outputColName": data["outputColName"],
	"url": data["url"],
	"wdcFile": data["wdcFile"],
	}